Image-to-Text
Safetensors
GGUF
English
idefics3
vision-language
credit-card
ocr
json-extraction
cardvault
smolvlm
fine-tuned
conversational
Instructions to use sugiv/cardvaultplus-500m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sugiv/cardvaultplus-500m with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf sugiv/cardvaultplus-500m:Q4_K_M # Run inference directly in the terminal: llama cli -hf sugiv/cardvaultplus-500m:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sugiv/cardvaultplus-500m:Q4_K_M # Run inference directly in the terminal: llama cli -hf sugiv/cardvaultplus-500m:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sugiv/cardvaultplus-500m:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sugiv/cardvaultplus-500m:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sugiv/cardvaultplus-500m:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sugiv/cardvaultplus-500m:Q4_K_M
Use Docker
docker model run hf.co/sugiv/cardvaultplus-500m:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sugiv/cardvaultplus-500m with Ollama:
ollama run hf.co/sugiv/cardvaultplus-500m:Q4_K_M
- Unsloth Studio
How to use sugiv/cardvaultplus-500m with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sugiv/cardvaultplus-500m to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sugiv/cardvaultplus-500m to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sugiv/cardvaultplus-500m to start chatting
- Docker Model Runner
How to use sugiv/cardvaultplus-500m with Docker Model Runner:
docker model run hf.co/sugiv/cardvaultplus-500m:Q4_K_M
- Lemonade
How to use sugiv/cardvaultplus-500m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sugiv/cardvaultplus-500m:Q4_K_M
Run and chat with the model
lemonade run user.cardvaultplus-500m-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Add GGUF README
Browse files- GGUF/README.md +31 -0
GGUF/README.md
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# GGUF Models
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This folder contains quantized GGUF versions of the CardVault+ model for efficient inference with llama.cpp.
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## Available Models
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| Model File | Size | Quantization | Use Case |
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|---|---|---|---|
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| `cardvault-500m-f16.gguf` | 783MB | F16 (Base) | Maximum quality |
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| `cardvault-500m-mmproj-f16.gguf` | 191MB | F16 (Vision) | **REQUIRED** |
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| `cardvault-500m-q8_0.gguf` | 417MB | Q8_0 | Near-perfect quality |
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| `cardvault-500m-q6_k.gguf` | 399MB | Q6_K | Balanced |
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| `cardvault-500m-q5_k_m.gguf` | 311MB | Q5_K_M | **Recommended** |
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| `cardvault-500m-q4_k_m.gguf` | 290MB | Q4_K_M | Maximum compression |
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## Usage
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```bash
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# Download llama.cpp
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git clone https://github.com/ggerganov/llama.cpp
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cd llama.cpp && make
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# Run inference (Q5_K_M recommended)
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./main \
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--model cardvault-500m-q5_k_m.gguf \
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--mmproj cardvault-500m-mmproj-f16.gguf \
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--image credit_card.jpg \
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--prompt "Extract card information in JSON format"
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```
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⚠️ **Critical**: Both text model + mmproj required for vision functionality!
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